Distributed ML and Security
HotDiSec: International Workshop on Hot Topics at the Intersection of Distributed Machine Learning and Security
In conjunction with
European Symposium on Research in Computer Security 2026
September 14-18, 2026
Sapienza University of Rome
Rome, Italy
Examines security questions raised by federated and distributed machine learning. Federated and distributed learning were motivated partly by privacy, but distributing training creates new attack surfaces: poisoning by participants, inference from updates, and no single party able to audit the result. Auditing is the structural problem, since the arrangement that protects participants’ data also prevents anyone from inspecting what they contributed.
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